On Perceptual Uncertainty in Autonomous Driving under Consideration of Contextual Awareness

Despite recent advances in automotive sensor technology and artificial intelligence that lead to breakthroughs in sensing capabilities, environment perception in the field of autonomous driving (AD) is still too unreliable for safe operation. Evaluating and managing uncertainty will aid autonomous vehicles (AV) in recognizing perceptual limitations in order to adequately react in critical situations. In this work, we propose an uncertainty evaluation framework in AD based on Dempster-Shafer (DS) theory, that takes context awareness into consideration, a factor that has been so far under-investigated. We formulate uncertainty as a function of context awareness, and examine the effect of redundancy on uncertainty. We also present a modular simulation tool that enables assessing perception architectures in realistic traffic use cases. Our findings show that considering context awareness decreases uncertainty by at least one order of magnitude. We also show that uncertainty behaves exponentially as a function of sensor redundancy.